Experiences with English-Hindi, English-Tamil and English-Kannada Transliteration Tasks at NEWS 2009
نویسندگان
چکیده
We use a Phrase-Based Statistical Machine Translation approach to Transliteration where the words are replaced by characters and sentences by words. We employ the standard SMT tools like GIZA++ for learning alignments and Moses for learning the phrase tables and decoding. Besides tuning the standard SMT parameters, we focus on tuning the Character Sequence Model (CSM) related parameters like order of the CSM, weight assigned to CSM during decoding and corpus used for CSM estimation. Our results show that paying sufficient attention to CSM pays off in terms of increased transliteration accuracies.
منابع مشابه
English to Indian Languages Machine Transliteration System at NEWS 2010
This paper reports about our work in the NEWS 2010 Shared Task on Transliteration Generation held as part of ACL 2010. One standard run and two non-standard runs were submitted for English to Hindi and Bengali transliteration while one standard and one nonstandard run were submitted for Kannada and Tamil. The transliteration systems are based on Orthographic rules and Phoneme based technology. ...
متن کامل$$-extension Hidden Markov Models and Weighted Transducers for Machine Transliteration
We describe in detail a method for transliterating an English string to a foreign language string evaluated on five different languages, including Tamil, Hindi, Russian, Chinese, and Kannada. Our method involves deriving substring alignments from the training data and learning a weighted finite state transducer from these alignments. We define an ǫ-extension Hidden Markov Model to derive alignm...
متن کاملEnglish-Hindi Transliteration Using Context-Informed PB-SMT: the DCU System for NEWS 2009
This paper presents English—Hindi transliteration in the NEWS 2009 Machine Transliteration Shared Task adding source context modeling into state-of-the-art log-linear phrase-based statistical machine translation (PB-SMT). Source context features enable us to exploit source similarity in addition to target similarity, as modelled by the language model. We use a memory-based classification framew...
متن کاملMining Transliterations from Wikipedia using Dynamic Bayesian Networks
Transliteration mining is aimed at building high quality multi-lingual named entity (NE) lexicons for improving performance in various Natural Language Processing (NLP) tasks including Machine Translation (MT) and Cross Language Information Retrieval (CLIR). In this paper, we apply two Dynamic Bayesian network (DBN)-based edit distance (ED) approaches in mining transliteration pairs from Wikipe...
متن کاملEnglish to Hindi Machine Transliteration System at NEWS 2009
This paper reports about our work in the NEWS 2009 Machine Transliteration Shared Task held as part of ACL-IJCNLP 2009. We submitted one standard run and two nonstandard runs for English to Hindi transliteration. The modified joint source-channel model has been used along with a number of alternatives. The system has been trained on the NEWS 2009 Machine Transliteration Shared Task datasets. Fo...
متن کامل